Methods and systems using mathematical analysis and machine learning to diagnose disease

ABSTRACT

Exemplified method and system facilitates monitoring and/or evaluation of disease or physiological state using mathematical analysis and machine learning analysis of a biopotential signal collected from a single electrode. The exemplified method and system creates, from data of a singularly measured biopotential signal, via a mathematical operation (i.e., via numeric fractional derivative calculation of the signal in the frequency domain), one or more mathematically-derived biopotential signals (e.g., virtual biopotential signals) that is used in combination with the measured biopotential signals to generate a multi-dimensional phase-space representation of the body (e.g., the heart). By mathematically modulating (e.g., by expanding or contracting) portions of a given biopotential signal, in the frequency domain, the numeric-based operation gives emphasis or de-emphasis to certain measured frequencies of the biopotential signals, which, when coupled with machine learning, facilitates improved diagnostics of certain pathologies.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a continuation application of U.S. Ser. No. 15/192,639, filedMar. 6, 2018, entitled “Methods and Systems Using Mathematical Analysisand Machine Learning To Diagnose Disease,” now U.S. Pat. No. 9,910,964,which claims priority to, and the benefit of, U.S. ProvisionalApplication No. 62/184,796, filed Jun. 25, 2015, titled “LatentTeratogen-Induced Heart Deficits Are Unmasked Postnatally withMathematical Analysis and Machine Learning on ECG Signals,” each ofwhich is incorporated by reference herein in its entirety.

TECHNICAL FIELD

The present disclosure relates to methods and systems to diagnosecardiac pathologies via mathematical and machine learning analysis onbiopotential signals.

BACKGROUND

Congenital heart defects (CHDs) are the most common birth anomaly, withventricular septal defects (VSDs) being the most prevalent category ofcongenital heart defects (CHDs). Clinically, about 80% of ventricularseptal defects (VSDs) resolve spontaneously within the first year, butlittle is known about the long-term consequences of these resolved VSDson postnatal heart function.

What are needed are devices, systems and methods that overcomechallenges in the present art, some of which are described above.

SUMMARY

Exemplified method and system facilitates monitoring and/or evaluationof disease or physiological state using mathematical analysis andmachine learning analysis of a biopotential signal collected from asingle electrode. The exemplified method and system creates, from dataof a singularly measured biopotential signal, via a mathematicaloperation (i.e., via fractional derivative calculation), one or moremathematically-derived biopotential signals (e.g., virtual biopotentialsignals) that is used in combination with the measured biopotentialsignals to generate a multi-dimensional phase-space representation ofthe body (e.g., the heart). In some embodiments, the fractionalderivative of a fraction of a real number or a fraction of an integernumber is applied numerically to the measured biopotential signals, or aportion thereof, in the frequency domain, to increase the dimensionalityof the measured signal data. In some embodiments, the fractionalderivative of an integer is applied numerically to the measuredbiopotential signals, or a portion thereof, in the frequency domain, toincrease the dimensionality of the measured signal data. Bymathematically modulating (e.g., by expanding or contracting) portionsof a given biopotential signal, in the frequency domain, thenumeric-based operation gives emphasis or de-emphasis to certainmeasured frequencies of the biopotential signals, which, when coupledwith machine learning, facilitates improved diagnostics of certainpathologies and facilitates diagnostics in real-time (or near realtime).

To this end, the exemplified method and system facilitates themeasurements of biopotential signals using a single electrode lead toobtain diagnostics results. In addition, the exemplified method andsystem may be used to enhance measurements collected by multiplephysical leads by, in effect, multiplying the physical effects withvirtual lead that provide a different vantage point or perspective fromthe original physical measurement that improves the subsequent analysis.

A clinical animal model study was performed, the study illustrates thatthe exemplified method and system facilitates clinically-relevantdiagnosis of physiologic conditions. In particular, the clinical animalmodel study illustrates that resolved congenital heart defects harboringhidden cardiovascular dysfunction can be detected using the exemplifiedmethod and system.

In an aspect, a method is disclosed of pre-processing data to extractvariables for use in machine learning to diagnose a pathology. Themethod includes receiving a biopotential signal data associated with asubject, said biopotential signal data being associated with abiopotential signal collected from one or more electrical leads;generating, via a processor, a first and a second fractional derivativesignal data by numerically performing one or more fractional derivativeoperations (e.g., a numeric fractional derivative operation) of thebiopotential signal data in a frequency domain and converting a resultof the one or more fractional derivative operations to a time domainsignal data, wherein each of the first and second generated fractionalderivative signal data comprises a same length and a same samplingfrequency as the biopotential signal data; and generating, via theprocessor, a three-dimensional space wherein each corresponding value ofthe biopotential signal data, the first fractional derivative signaldata, and the second fractional derivative signal data forms athree-dimensional point in said space, wherein geometric features anddynamical properties of the three-dimensional space are used asvariables representative of the subject in machine learning to detectone or more diagnosable pathology of the subject.

In some embodiments, the first fractional derivative signal data isgenerated by performing a first numeric fractional derivative of a firstorder value on the biopotential signal data in the frequency domain andby performing an inversed transformation (e.g., inversed FFT) on thefractional derived signal data to convert the fractional derived signaldata to a time domain signal data. In some embodiments, the inversedtransformation comprises an inversed Fast Fourier Transform (inversedFFT) operation.

In some embodiments, the second fractional derivative signal data isgenerated by performing a second numeric fractional derivative of asecond order value on the biopotential signal data in the frequencydomain and by performing an inversed transformation (e.g., inversed FFT)on the fractional derived signal data to convert the fractional derivedsignal data to a time domain signal data.

In some embodiments, each of the first fractional derivative signal dataand the second fractional derivative signal data comprises a time domainsignal data.

In some embodiments, the first fractional derivative signal data isgenerated by a fractional derivative of an order of pi/2.

In some embodiments, the second fractional derivative signal data isgenerated by a fractional derivative of an order of 0.5.

In some embodiments, the geometric features and dynamical properties ofthe three-dimensional space are generated by performing a MMP (modifiedmatching pursuit) algorithm of the three-dimensional point in saidspace.

In some embodiments, the biopotential signal data is associated with abiopotential signal collected from a single electrical lead.

In some embodiments, the single electrical lead collected measurementsof the biopotential signal at a location selected from the groupconsisting of a chest line of the subject, a waistline of the subject, awrist of the subject, a pelvic line of the subject, a neck of thesubject, an ankle of the subject, a forehead of the subject, and an armline of the subject.

In some embodiments, the method includes generating, via a processor, analpha shape of the three-dimensional point in said space, wherein thegeometric features and dynamical properties of the three-dimensionalspace includes the geometric features of the alpha shape.

In some embodiments, the method includes generating a Delaunay trianglemesh of the three-dimensional point in said space, wherein the geometricfeatures and dynamical properties of the three-dimensional spaceincludes the geometric features of the Delaunay triangle mesh.

In some embodiments, the biopotential signal data compriseselectrocardiogram (ECG) data.

In some embodiments, the machine learning analysis comprises using anartificial neural network algorithm or a regression random forestalgorithm.

In another aspect, a method is disclosed of pre-processing data toextract variables for use in machine learning to diagnose a pathology.The method includes receiving biopotential signal data associated with asubject, said biopotential signal data being associated withbiopotential signals collected from two or more electrical leads;generating, via a processor, a fractional derivative signal data bynumerically performing one or more fractional derivative operations ofat least one of the biopotential signal data in a frequency domain andconverting a result of the one or more fractional derivative operationsto a time domain signal data, wherein the generated fractionalderivative signal data comprises a same length and a same samplingfrequency as the at least one of the biopotential signal data; andgenerating, via the processor, a three-dimensional space wherein eachcorresponding value of each of the biopotential signal data and thefractional derivative signal data forms a three-dimensional point insaid space, wherein geometric features and dynamical properties of thethree-dimensional space are used as variables representative of thesubject in machine learning to detect one or more diagnosable pathologyof the subject.

In some embodiments, each of the two or more electrical leads collectedmeasurements of the biopotential signal at a location selected from thegroup consisting of a chest line of the subject, a waistline of thesubject, a wrist of the subject, a pelvic line of the subject, a neck ofthe subject, an ankle of the subject, a forehead of the subject, and anarm line of the subject.

In another aspect, a method is disclosed of determining congenital heartdefects (CHD) in a mammal. The method includes receiving biopotentialrecordings associated with the mammal, the biopotential recordings beingrecorded at predetermined intervals; developing variables associatedwith the biopotential recordings to create a dataset; and analyzing thedataset to determine if the mammal has a CHD.

In some embodiments, the biopotential recordings associated with themammal are recorded using a measuring equipment comprising a singlesurface lead.

In some embodiments, a measuring equipment comprises an intracardiacelectrogram instrument.

In some embodiments, the measuring equipment comprises a smart watch orfitness heart band.

In another aspect, a system is disclosed that includes that includesremote storage (e.g., storage area network) configured to receivebiopotential data from a network-connected biopotential measuringapparatus; one or more processors; and a memory having instructionsstored thereon, wherein the instructions, when executed by theprocessor, cause the processor to: generate phase space variablesassociated with the biopotential data; analyzing the phase spacevariables to determine if the mammal has a CHD.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of a system for pre-processing data to extractvariables for use in machine learning to diagnose a pathology, inaccordance with an illustrative embodiment.

FIG. 2 is a diagram of a method for processing data to diagnose apathology, in accordance with an illustrative embodiment.

FIG. 3 is a detailed diagram of a method of pre-processing the data asshown in FIG. 2, in accordance with an illustrative embodiment.

FIG. 4 is a detailed diagram of a method of signal data normalization asshown in FIG. 3, in accordance with an illustrative embodiment.

FIG. 5, comprising FIGS. 5A and 5B, is a detailed diagram of methods ofvirtual signal generation as shown in FIG. 3, in accordance with anillustrative embodiment.

FIG. 6 is a detailed diagram of a method of performing phase spaceanalysis as shown in FIG. 2, in accordance with an illustrativeembodiment.

FIG. 7 is a diagram illustrating postnatal VSD resolution, in accordancewith an illustrative embodiment, in accordance with an embodiment, inaccordance with an embodiment.

FIG. 8 is a diagram illustrating hypothesis of a clinical study, inaccordance with an embodiment.

FIG. 9 is a diagram illustrating a design of experiment for the clinicalstudy described in relation to FIG. 8, in accordance with an embodiment.

FIG. 10 is a diagram illustrating a method for conducting the experimentfor the clinical study described in relation to FIG. 9 using machinelearning process, in accordance with an embodiment.

FIG. 11 is a diagram illustrating a vectorcardiogram including virtualbiopotential signals generated from an exemplified fractional derivativeoperation, in accordance with an embodiment.

FIG. 12 is a diagram illustrating exemplary inputs to the machinelearning processes described in relation to FIG. 10, in accordance withan embodiment.

FIG. 13 is a diagram illustrating a phase space model of avectorcardiogram of a specimen with a diagnosable disease.

FIG. 14 is a diagram illustrating an alpha-shape phase space model of avectorcardiogram of a control specimen.

FIGS. 15 and 16 are diagrams illustrating results of the clinical studyas described in relation to FIGS. 7-10, in accordance with anillustrative embodiment.

FIGS. 17A and 17B show the performance of heart rate variability asassessed using a receiver-operator characteristic curve in theprediction of DMO-exposed or chemical naïve status, using either thestandard deviation method, or Poincare Pearson correlation method.

FIG. 18 show the performance of the artificial neural network ondistinguishing the DMO-exposed data from the chemical naïve data asassessed using a receiver-operator characteristic curve.

FIG. 19 shows the performance of the random forest on distinguishing theDMO-exposed data from the chemical naïve data within the “leave-one-out”validation paradigm, as assessed by a receiver operator characteristiccurve.

DETAILED DESCRIPTION

The components in the drawings are not necessarily to scale relative toeach other and like reference numerals designate corresponding partsthroughout the several views:

FIG. 1 is a diagram of a system 100 for pre-processing data to extractvariables for use in machine learning to diagnose a pathology, inaccordance with an illustrative embodiment. As shown in FIG. 1, thesystem 100 includes a biopotential measuring equipment 102 and ananalysis subsystem 104. The biopotential measuring equipment 102collects a biopotential signal via a single lead electrode 106 (andcorresponding GND lead 124) that is attached to the surface of a subject108 (e.g., the skin of a test animal or a person). The biopotentialmeasuring equipment may be any device configured to captureelectrophysiological signal. In some embodiments, a Holter ECG monitoris used as the biopotential measuring equipment 102 for measuring andrecording the biopotential signal.

In some embodiments, the single lead electrode 106 comprise a surfaceelectrode that is placed directly, or indirectly, on the surface of theskin or body tissue to record electrical activity. In some embodiments,the single lead electrode 106 comprises electrodes that are integratedinto wearable devices to contact the skin when the wearable device iswore or attached to a patient or subject.

It should be appreciate that other wiring topology may be used withoutdeparting from the spirit of the disclosure. In some embodiments, theGND lead is a common-mode return. In other embodiments, the GND lead mayserve as a differential mode signal with lead 106. In other embodiments,the GND lead is referenced to (or returned through) the earth, thechassis, or a shield.

Biopotential signals, in some embodiments, are electric potential thatis measured between points on a tissue. Examples of biopotential signalincludes an ECG (electrocardiogram) signal that is used to assesselectrical and muscular functions of the heart.

In some embodiments, the biopotential measuring equipment is a wearabledevice that is configured to measure and to record the biopotentialsignal. In some embodiments, the wearable device is configured to beplaced at a chest line of a subject, a waistline of the subject, a wristof the subject, a pelvic line of the subject, a neck of the subject, anankle of the subject, a forehead of the subject, and an arm line of thesubject and has electrodes positioned to be in proximal contact with theskin or surface of the wearer. The wearable device may have a housing inthe form of watch, an arm band, a neck band, a leg band, a chest band, ahead band and such. In other embodiments, the biopotential measuringequipment is a part of an exercise equipment (e.g., a handle bar), aweight scale, a mat, or any like device that contacts the skin orsurface of a person.

Referring still to FIG. 1, the biopotential signals 110 are stored asbiopotential signal data 112. The analysis system 104 receives thebiopotential signal data 112, in some embodiments, over a network, fromthe biopotential measuring equipment 102. In some embodiments, theanalysis system 104 receives the biopotential signal data 112 from astorage area network (SAN). In other embodiments, the analysis system104 and biopotential measuring equipment 102 are located a singledevice, e.g., a wearable device. Other configurations may be used.

Referring still to FIG. 1, the analysis system 104 is configured togenerate, from the source biopotential signal data 112 of a singlesignal, one or more additional biopotential signal data (shown as afirst fractional derivative signal data 114 a and a second fractionalderivative signal data), via a fractional calculus operation 116, of thesource biopotential signal data 112, where each of the first and secondgenerated fractional derivative signal data 114 a, 114 b comprises asame length and a same sampling frequency as the biopotential signaldata. In some embodiments, the numerical fractional derivative operationis performed to emphasize or deemphasize certain frequency componentsand such that there is an absence of orthogonality in the resultingvectors. The additional biopotential signal data are used in conjunctionwith the source biopotential signal data to generate a phase space mapto be used in subsequent phase space analysis 118 later describedherein. The output of the phase space analysis are then evaluated usingartificial neural networks 120 to assess parameters 122 associated witha presence of a disease or physiological characteristic. The output ofthe processor is then transmitted to a graphical user interface forvisualization. The graphical user interface, in some embodiments, isincluded in a display unit configured to display parameters 122. In someembodiments, the graphical user interface displays intermediateparameters such as a 3D phase space plot representation of thebiopotential signal data and virtual biopotential signal data.

FIG. 2 is a diagram of a method 200 of FIG. 1 for processing data todiagnose a pathology, in accordance with an illustrative embodiment. Asshown in FIG. 2, the method 200 includes collecting 200 signal data andpre-processing 204 the signal data to generate a phase space dataset tobe used in phase analysis 206, whereby features of the phase spacedataset are extracted and evaluated via an artificial neural networkanalysis 208.

FIG. 3 is a detailed diagram of the pre-processing 204 of the data asshown in FIG. 2, in accordance with an illustrative embodiment. Thepre-processing 204 includes, in some embodiments, signal datanormalization 302 and virtual signal generation 304.

FIG. 4 is a detailed diagram of a method of signal data normalization asshown in FIG. 3, in accordance with an illustrative embodiment. As shownin FIG. 4, the signal normalization 302 includes an initial step of datachannel removal 402 and Hurst exponent filtering 404 of the sourcebiopotential signal data 112. Further detail of the data channel removaland Hurst exponent filtering is described in Matteo et al., “Scalingbehaviors in differently developed markets,” Physica A, 324, pg. 183-188(2003). In some embodiments, the signal normalization 302 furtherincludes selecting 406 a cleanest segment of the filtered signal, thesegment having a minimized residue between a wavelet model, of thesignal, that is designed to detect the presence of a non-biologicalnoise and the signal itself. For example, a segment comprising acleanest 5-second window of a 10 second recording interval may beselected. In some embodiments, the wavelet model is configured todecompose the signal into temporal levels in wherein one or more of thehighest levels of decomposition of the temporal levels are used in thesubtraction of the wavelet model from the signal to determine theresidue. In some embodiments, the signal normalization 302 furtherincludes filtering 408 using a second wavelet model to remove undesirednoise (e.g., any remaining noise) in the selected cleaned segment. Thesecond wavelet model is configured decompose the signal into a number oftemporal levels and one or more of the highest level of decompositionare maintained. It should be appreciated by one skilled in that art thatother signal data normalization may be used.

FIG. 5 is a detailed diagram of a method of virtual signal generation asshown in FIG. 3, in accordance with an illustrative embodiment. As shownin FIG. 5, the virtual signal generation 304 includes creating one ormore virtual signal data such that the data interact with the originalsignal data to create a valid phase space portrait in which the limitcycles of the input biopotential signal (e.g., cardiac cycle) areoverlaid in 3-dimensional space and there was an absence oforthogonality in the resulting vector. For example, where a singlesource biopotential signal data is available, the virtual signalgeneration 304 may be used to generate two virtual biopotential signalswhere a valid phase space portrait is in 3 dimensional space. In anotherexample, where two source biopotential signal data are available, thevirtual signal generation 304 may be used to generate a virtualbiopotential signal where a valid phase space portrait is in 3dimensional space. In some embodiments, the valid phase space portraitmay be in dimensional space greater than 3, such as 4, 5, 6, 7, 8, 9,10, or greater.

In some embodiments, each of the one or more virtual signal data isgenerated by performing a Fast Fourier Transform 502 on the normalizedsignal data. A numerical fractional derivative is then performed 504 oneach of the FFT signal data and an inversed Fast Fourier Transform(inversed FFT) is performed 506 on that output. Examples of the order ofthe fractional derivative include pi/2 or 0.5. In some embodiments, theorder of the fractional derivative is a fraction of a real number orcomplex number. In some embodiments, the order of the fractionalderivative is a fraction of an integer. In some embodiments, the outputof the inversed FFT is further processed to remove 508 baseline wander.

In some embodiments, each of the one or more virtual signal data isgenerated by performing a numerical fractional differencing on thenormalized signal data, implemented through the use of a convolution. Arational transfer function is defined to correspond to the specifiedorder of the fractional derivative, which is then applied to the inputdata through the use of a digital filter configured to accept suchinput. Examples of the order of the fractional derivative include pi/2or 0.5. In some embodiments, the order of the fractional derivative is afraction of a real number or complex number. In some embodiments, theorder of fractional derivative is a fraction of an integer. In someembodiments, the output of the convolution is further processed toremove 508 baseline wander.

FIG. 6 is a detailed diagram of the method of performing phase spaceanalysis as shown in FIG. 2, in accordance with an illustrativeembodiment. In some embodiments, the input to the phase space analysisis a point-cloud phase space map 601 of the biopotential signal data andcorresponding virtual signal data as a vectorcardiogram. In someembodiments, the phase space analysis includes performing modifiedmatching pursuit (MMP) algorithm 602. The MPP algorithm 602 may be usedto generate a sparse mathematical model 604. Detail of the MMP algorithmis provided in Mallat et al., “Matching Pursuits with Time-FrequencyDictionaries,” IEEE Transactions on Signal Processing, Vol. 41 (12),Pages 3397-2415 (1993).

Characteristics of this model may be extracted, in a feature extractionoperation 606, to determine geometric and dynamic properties of themodel.

In some embodiments, the point-cloud phase space map 601 is encapsulatedby alpha shape or a Delaunay triangulation. Features of the alpha shapeand/or triangulation may be extracted, via feature extraction 610, todetermine additional geometric and dynamic properties of the model.

In some embodiments, the extracted geometric and dynamic properties ofthe alpha shape and modified matching pursuit operations are use asvariables to an artificial neural network analysis, a regression randomforest analysis, or other machine learning analyses.

Experiment—Latent Teratogen-Induced Heart Deficits

Using an animal model described below, the exemplified system and methodof using mathematical analysis and machine learning to diagnose diseaseis shown. Specifically, the exemplified system and method employsfractional calculus to increase the dimensionality of a single lead ECGvia, a numerical method with the inverse FFT to be used in subsequentphase space analysis.

The exemplified system and method was shown to successfully extractmeaningful variables from biopotential signals, in an animal model,specifically those from electrocardiogram data collected from animplanted radiotelemeter, at a time when teratogen-exposed test animalsare clinically indistinguishable from controls. Machine learning wasthen leveraged to predict, within a robust validation framework, thepresence of latent cardiovascular dysfunction. The test illustrates thatthe exemplified system and method can be used to analyze data (e.g.,single time series data) from single lead measurements and to generatehigher level of dimensional phase space data in a subsequent phase spaceanalysis.

In the animal model, test rats were treated in such a way as to induce ahigh incidence of CHD (congenital heart defects) in offspring. Damsdelivered naturally, and the heart structure and function were assessedin female pups using echocardiography on postnatal day (PND) 4, PND 21and PND 56. At postnatal day (PND) 56, radiotelemetry units wereimplanted into 9 treated rats and 8 control rats. Two weekspost-surgery, telemeters were activated and ECG recordings werecontinuously collected every 10 seconds and every 12 seconds over aperiod of two weeks. 50,000 collected data points per rat were eachtransformed, from the single ECG recording, into a uniquethree-dimensional phase space dataset, and machine learning used tocreate predictive algorithms capable of identifying differences in heartfunction between control and treated rats and other mammals.

The results, as described in more detail below, demonstrate that ateratogen-induced CHD that resolves postnatally and results in heartsthat appears normal by conventional measures are, in fact, differentfrom teratogen-naïve hearts. As equally important, the results revealthat fractional calculus may be used to increase the dimensionality of asingle lead biopotential signal for use in phase space analysis.

Experiment Setup

FIG. 7 is a diagram illustrating postnatal ventricular septal defect(VSD) resolution, in accordance with an illustrative embodiment. Asshown in FIG. 7, two cohorts of animals are selected with significantcardiovascular differences that decline to an absolute minimum at 8-10weeks postnatal when these differences in the animals are clinicallyindistinguishable. The animals are used to verify that the exemplifiedsystem and method may be used to identify which animals were affected bydrug administration (i.e., during this 8-10 week postnatal period whenthe cardiovascular differences in the animals are clinicallyindistinguishable).

FIG. 8 is a diagram illustrating hypothesis of a clinical study, inaccordance with an embodiment. As shown in FIG. 8, the hypothesis isthat exemplified mathematical analysis and machine learning will revealfunctional deficits between the cohort of offspring with treated damsand the cohort of offspring with non-treated dams at a time when it isotherwise undetectable by conventional analysis (during the 8-10 weekperiod) without the need for a conventional stressor.

FIG. 9 is a diagram illustrating a design of experiment for the clinicalstudy described in relation to FIG. 8, in accordance with an embodiment.As shown in FIG. 9, the test rats were divided into two cohorts: exposedand unexposed to dimethadione (DMO). The test rates were allowed to cometo natural parturition; the pups were evaluated over the first 8 weeksof life using echocardiography. Then, at 8 weeks, radiotelemeters weresurgically implanted into the test rats to measure several physiologicalsignals including ECG signals. The ECG signals were then used in machinelearning to create a predictor to discriminate between the two cohorts.

FIG. 10 is a diagram illustrating a method for conducting the experimentfor the clinical study described in relation to FIG. 9 using machinelearning process, in accordance with an embodiment. As shown in FIG. 10,the exemplified experiment begins with test rats with known exposurepaired to ECG data. Variables were then developed from the ECG data, andinput to a machine learning process. The exemplified method and systemis used to predict if the rat that generated that ECG was exposed orunexposed to dimethadione (DMO).

FIG. 11 is a diagram illustrating a vectorcardiogram including virtualbiopotential signals generated from an exemplified fractional derivativeoperation, in accordance with an embodiment. As shown in FIG. 11, theexemplary inputs include the original measured ECG signal data 112 andthe virtual ECG signal data 114 a, 114 b.

FIG. 12 is a diagram illustrating exemplary inputs to the machinelearning processes described in relation to FIG. 11, in accordance withan embodiment. As shown in the FIG. 12, a phase space dataset 1202 isshown of the vectorcardiogram (VCG) that includes the measuredbiopotential signals 112 and the virtual biopotential signals 114 a, 114b generated from the exemplified fractional derivative operation. Thevectorcardiogram (VCG) is shown in a point-cloud phase space map 1202 inwhich the measured biopotential signals 112 and the virtual biopotentialsignals 114 a, 114 b are shown without time in a three axis coordinatesystem. The phase space dataset 1202 is quantified by being wrapped in ageometric shape, for example, an alpha shape or a Delaunay triangle.Detail of an alpha shape operation is described in Edelsbrunner et al.,“Three-dimensional alpha shapes,” ACM Transactions on Graphics, Vol. 13(1): 43-72 (1994).

FIG. 13 is a diagram illustrating an alpha-shape phase space model of avectorcardiogram of a specimen with a diagnosable disease. FIG. 14 is adiagram illustrating an alpha-shape phase space model of avectorcardiogram of a control specimen. As shown in FIGS. 13 and 14,there are visual differences between datasets of the exposed andunexposed test rats, but these differences are subtle and are moreconducive to be analyzed via machine learning to expose suchdifferences.

In some embodiments, the machine learning analysis uses families ofvariables including data shape variables, fractional derivatives, signalmodeling using MMP, phase space modeling, and dynamical systemvariables. In the exemplified experiment, with data for 17 test rats,each with 50,000 ECG signals, where each ECG signal has 250 variables,there are about two billion data points used in the analysis includingan artificial neural network.

Discussion

Congenital heart defects (CHD) are the most common class of congenitalanomalies with an incidence of approximately 1.9-7.5% of live births.The most prevalent birth defect is the ventricular septal defect (VSD)at 25-40% of all CHD, in which the septum fails to close between theleft and the right ventricles. Postnatal persistence or surgical repairof VSD or other CHD significantly increases the lifetime risk of heartdisease in these patients relative to unaffected cohorts, and thereforenecessitates a vigilant lifetime of observation and potentialintervention by a cardiologist specializing in the care of CHD.Interestingly, approximately 80% of VSDs that present clinically resolvespontaneously within the first year of life. While a clinical resolutionof the structural damage is a favorable outcome, there is a dearth ofinformation about the long-term functional consequences of resolved VSD.

To explore the potential deleterious long-term consequences of resolvedCHDs, the exemplified rat model was developed that recapitulates many ofthe clinical presentations of CHD. To generate such a model, pregnantrats were treated with a chemical teratogen. It has been estimated that80% of in utero trimethadione exposures resulted in embryo/fetal loss,or malformations including a high incidence of CHD. Without wishing tobe bound to particular theory, it is thought that DMO teratogenicity ismediated by oxidative stress which might be the result of the result ofhypoxia reperfusion injury caused by bradycardias induced by disruptionof calcium and potassium ion channels in embryonic myocytes. It has beendemonstrated in outbred pregnant Sprague-Dawley rats that the incidenceand severity of CHD in progeny are highly dependent upon the gestationalwindow and total exposure to DMO. The administration of four 300 mg/kgdoses every twelve hours beginning on the morning of gestation day (GD)9 produces approximately a 50% incidence of CHD if fetuses are examinedon GD 21, one day prior to natural parturition (see, e.g., Weston etal., “Co-variation in frequency and severity of cardiovascular andskeletal defects in Sprague-Dawley rats after maternal administration ofdimethadione, the N-demethylated metabolite of trimethadione. Birthdefects research Part B,” Developmental and reproductive toxicology,Vol. 92, Pages 206-15 (2011)). When similarly treated dams are allowedto deliver their progeny naturally and the cardiac structure andfunction of the pups is evaluated longitudinally by high-resolutionechocardiography, a scenario reminiscent of the clinical presentation ofCHD is revealed. For example, in infants, approximately 80% of VSDresolves by one year of age, and in test rats after DMO exposure, about80% of the VSD resolve spontaneously by weaning. Other structuralelements such as left ventricular (LV) mass, LV anterior wall thicknessin systole (LVAW;s) and LVAW in diastole (LVAW;d) are all significantlydifferent between control and DMO-exposed pups close to the time ofparturition, but resolve over time such that by 10 weeks of age controland DMO animals are indistinguishable. In the rat pups exposed to DMO,cardiac dysfunction is pronounced close to parturition, but graduallyresolves, such that by 10 weeks of age under basal, during unstressedconditions, cardiac output (CO), stroke volume (SV), ejection fraction(EF), fractional shortening (FS) and pulmonary artery regurgitation PAregurg. (mm/s) have all normalized. At this point, radiotelemeterscapable of measuring continuous single channel electrocardiogram (ECG)were surgically implanted into the rats. After two weeks of surgicalrecovery, baseline heart function was obtained and the animals weremated. Pregnancy is a physiological challenge for the maternalcardiovascular system and clinically has been referred to as acardiovascular “stress test”. Pregnancy-induced changes to the mammalianmaternal CV system include a 30-40% increase in blood volume, 30-60%increase in cardiac output (CO), transient cardiac hypertrophy anduterine spiral arterial (SA) remodeling ref. Cardiac hypertrophyoccurring in normal pregnancy is physiological and reversible, similarto exercise-induced hypertrophy.

Disconcertingly, the cardiovascular systems of test rats exposed to DMOin-utero adapted poorly to the burden of pregnancy. Manifestationsincluded altered CO, SV, radial and longitudinal strain, and elevatedmean arterial pressure at the time of spiral artery remodeling. Thus,the function of hearts with resolved CHD were indistinguishable fromcontrol hearts under basal unstressed conditions; however, under thestress of pregnancy profound cardiac and hemodynamic deficienciesemerged.

In the exemplified experiment, in-utero exposure to the heart teratogenDMO was used to generate a population of rats with resolved CHD thatonly presented with cardiac dysfunction during the burden of pregnancy.The exemplified method and system was used to predict the presence oflatent teratogen-induced cardiac functional deficits prior to the onsetof pregnancy, without the use of a cardiovascular stress test, and usingthe ECG data collected via the telemeter within the ten to twelve weekperiod of the study previously described. There are no discernabledifferences between the cohorts during this ten to twelve week period.

Experiment Results

FIGS. 15 and 16 are diagrams illustrating results of the clinical studyas described in relation to FIGS. 7-10, in accordance with anillustrative embodiment. Specifically, FIG. 15 is a plot of a predictiveoutput of the neural network analysis for the two cohorts of testanimals, which were by conventional measures, indistinguishable. Theplot shows all the predictions on the treated and control cohorts testdata (recall the 85%), where there is a clear visual separation betweenthe baselines. For comparison, a heart-rate variability (HRV) analysiswas run that uses landmarks in the ECG signal data to automaticallydetect high content data. As shown, HRV was not successful inidentifying the cohorts, while the exemplified method and system (usingthe machine learning predictor) was found to be significantly better,showing that it is possible to discriminate between these two groups,and therefore they are different.

Specifically, the exemplified experiment was conducted with two cohortsin which five test rats were exposed to DMO in utero (Cohort #1), andeight control rats were chemical naïve (Cohort #2). Raw ECG signals wererecorded from a single channel with a sampling frequency of 1000 Hz.Data acquisition was blocked into ten-second intervals from the testrats for a two week period between the ages of eleven to fourteen weeks,when the two cohorts were otherwise clinically indistinguishable usingechocardiography or heart rate variability (HRV). The statisticalevaluation of the predictors used to discriminate between the cohorts,such as HRV, primarily utilized area under the receiver operatorcharacteristic curve (AUC). AUC examines the performance of a predictoragainst a binary target variable, which in this case, is the DMO-exposedor chemical naïve status of the rat. An AUC value of 0.5 represents arandom prediction, an AUC of 1 is a perfect prediction, and an AUC of 0is a perfect, but inverted, prediction (where all data points of oneclass are predicted to be the other class, and vice versa).

HRV reflects beat-to-beat changes in heart rate reflecting dynamicchanges in autonomic tone. HRV is a clinically usefulelectrophysiological endpoint and is the foundation for riskstratification strategies such as REFINE, which seeks to determinewhether non-invasive physiological parameters collected after theoccurrence of a myocardial infarction (MI) predict the subsequentincidence of cardiac death or resuscitated cardiac arrest. In the REFINEstudy, HRV had an AUC of 0.62 and hazard ratio of 2.15 in the 10-14 weekperiod post-MI, proving to be a somewhat useful technique for riskstratification, which led us to believe it might provide utility in theidentification of DMO-exposed rats.

FIGS. 17A and 17B show the performance of heart rate variability asassessed using the receiver-operator characteristic curve in theprediction of DMO-exposed or chemical naïve status, using either thestandard deviation method (FIG. 17A), or Poincare Pearson correlationmethod (FIG. 17B). FIGS. 17A and 17B were generated as follows. Afive-second window that minimized non-biological noise was identified ina ten-second recording interval of a given test rat. Heartbeats wereidentified during this five-second window, and a confidence score (CS)determined; five-second windows possessing identified heartbeats with aCS greater than 0.7 were included in statistical evaluation. Afterheartbeat identification, HRV was calculated using the most widelyaccepted approach in which the standard deviation of the R-R interval(referencing the standard PQRST notation to represent the five waveformsin the cardiac cycle) was calculated for each five-second window.Additionally, a Poincaré plot was created in which the length of the R-Rinterval and the length of the next R-R interval create a point intwo-dimensional scatter plot and the Pearson correlation of this data iscalculated. The performance statistics of these two HRV techniques arereported in Table 1, with a threshold applied for statistics and thecorresponding receiver operator characteristic curves are plotted inFIGS. 17A and 17B.

Table 1 shows statistical performance of heart rate variability in theprediction of DMO-exposed or chemical naïve status. The AUCs of thesetwo predictors demonstrate that quantifying HRV using either method haslow or no predictive power. The poor performance is additionallyreflected in the diagnostic odds ratio (DOR), which is only slightlyabove 1 (where 1 indicates there is no change in the relative odds ofthe rat being DMO-exposed, given a positive test result).

TABLE 1 HRV: Poincare HRV: Standard Pearson Statistic DeviationCorrelation AUC 0.51 0.59 Sensitivity 78%  2% Specificity 23% 99%Positive Predictive 39% 48% Value Negative Predictive 62% 62% ValueDiagnostic 1.05 1.47 Odds Ratio (95% CI 1.03-1.07) (95% CI 1.37-1.58)Number of rats 4/13 8/13 correctly classified Percent of intervals 44%61% correctly classified

FIG. 18 show the performance of the exemplified method suing artificialneural network on distinguishing the DMO-exposed data from the chemicalnaïve data, on the validation data when 15% of the data is allocated tothe training set and the remaining 85% is allocated to validation set,as visualized by the receiver operator characteristic curve. Inaddition, Table 2 shows the statistical performance of the artificialneural network on distinguishing the DMO-exposed data from the chemicalnaïve data, on the validation data when 15% of the data is allocated tothe training set and the remaining 85% is allocated to validation set.

TABLE 2 Statistic Value AUC 0.79 Sensitivity 70% Specificity 73%Positive Predictive 58% Value Negative Predictive 82% Value DiagnosticOdds Ratio 6.26 (95% CI 6.16-6.35) Number of rats correctly 12/13classified Number of intervals 72% correctly classified

ANNs are widely used in machine learning as they enable hithertounachievable artificial intelligence benchmarks. A supervised ANN learnsto predict a target using a vector of inputs in a way that mimics humanneural processing. The ANN contains neurons arranged in a series ofconnected layers; firstly, the input layer that accepts the features,then one or more hidden layers to capture the non-linearity of thefunction being modeled, followed by the output layer that predicts thevalue of the target which corresponds to the input vector. The neuronsin the ANN are stimulated by the input vector, and transmit thosestimuli downstream to following layers depending on the value of thefeatures, the strength of the connection between the neurons, and theactivation function found within the neuron. ANNs may be used as auniversal functional approximator.

To explore the possibility of creating a more robust prediction model,an ensemble machine-learning algorithm based on random forest was usedwhich relies on the principle that a series of weak learners, or simplealgorithms that describe a limited amount of complexity in the data,when used in combination have powerful generalization properties. Randomforest is composed of decision trees as weak learners. Decision treesare tree structures where the nodes are decisions (or specific to thisapplication, a threshold on a feature), and upon taking a decision theconnection to that child node is followed, which reveals a sub-decisiontree with the original child as the root. The process continues untilarrival at a leaf node, which is the final prediction on the data. Eachdecision tree is exposed to a different partition of the both therecording intervals and the feature vector, and therefore tends to learna distinct facet of the problem. A regression random forest algorithmwith 100 decision trees was trained using the validation strategy justdescribed, the results of which are shown in Table 3, and visualized inFIG. 19.

FIG. 19 shows the performance of the random forest on distinguishing theDMO-exposed data from the chemical naïve data within the “leave-one-out”validation paradigm, as assessed by the receiver operator characteristiccurve. As shown in FIG. 19, the performance is diminished slightly ascompared to that of the first validation strategy as described inrelation to FIG. 18, but in the context of this robust validationstrategy it is still highly predictive. The performance is the result ofanalyzing the predictions produced in amalgamation by thirteen randomforests (one for each of the thirteen test animals), each predicting ononly the intervals of the single rat it did not receive in the trainingphase. The threshold, for those statistics requiring it, was set usingthe boundary that provided the maximal classification accuracy. Whenreserving a DMO-exposed rat for testing, the training set is composed ofthe 8 chemical naïve rats and the remaining 4 DMO-exposed rats,resulting in a ratio of 2:1.

Table 3 shows the statistical performance of the random forest ondistinguishing the DMO-exposed data from the chemical naïve data withinthe “leave-one-out” validation paradigm.

TABLE 3 Random Statistic Forest AUC 0.73 Sensitivity 40% Specificity 99%Positive Predictive Value 97% Negative Predictive Value 70% DiagnosticOdds Ratio 80.4 (95% CI 77.0-84.0) Number of rats correctly 10classified Number of intervals correctly 74% classified

Experiment—Material and Methods

The analytical methodology described herein used pre-processing in thetransition from data collection to feature extraction. Data channelremoval and Hurst exponent filtering are the initial operations. Theideal threshold on which to accept data was observationally determinedto be 0.7 on that exponent, which ranges from 0 to 1, through the visualinspection of a representative subset of the ECG signals along with thecalculated exponents of those signals. Second, the cleanest 5-secondwindow out of the 10-second recording interval was found by selectingthe segment that minimizes the residue between a wavelet model of thesignal, designed to detect the presence of non-biological noise, and thesignal itself. The wavelet model is computed using the functionality ofthe MATLAB™ (MATHWORKS; Natick, Mass.) Wavelet Toolbox. Data outside ofthis 5-second segment is discarded.

In some embodiments, the filtering provides a clean signal that lendbetter to numerical operators (e.g., numerical fractional derivativeoperations). It should be appreciated by those skilled in the art thatother filtering and signal cleaning operations may be used.

Following this, any remaining noise in this 5-second interval is removedusing a second wavelet model, which is designed for noise removal ratherthan noise detection, but is otherwise similar to the model used for theselection of the 5-second segment. Both of the wavelet models decomposethe signal into eight temporal levels, but the noise detection modelonly preserves the two highest levels of decomposition (resulting inefficient capture of noise when subtracting the wavelet model from thesignal), while the noise removal model at least partially maintains thefour highest levels of decomposition. The phase space reconstruction,and therefore the creation of the vVCG, was then performed through thecreation of the two virtual signals. Transformations to create the twovirtual signals were chosen such that the signals interact with theoriginal signal to create a valid phase space portrait, where the limitcycles of the cardiac cycles were overlaid in 3-dimensional space andthere was an absence of orthogonality in the resulting vectors. Thesevirtual leads were created by taking derivatives of the acquired lead insuch a way as to create a valid phase space portrait, or a shape inthree-dimensional space where the values of each of the three leads at agiven time form a three-dimensional point in that space. Fractionalderivatives of order pi/2 and 0.5 were found to be suitable, as computednumerically through the conversion of the signal into the frequencydomain using the Fast Fourier Transform (FFT), calculation of therequired derivative within the frequency domain, and conversion of thederivative back to the time domain using the inverse FFT. The baselinewander was then extracted from each of the three dimensions through theuse of a median filter with an order of 1500 ms, smoothed with a 1-Hzlow-pass ideal filter, and subtracted from the signals. The bias wasthen removed from the resulting signals by subtracting estimations ofthe modes of the signals using the maximums of the probability densitiescalculated with a kernel smoothing function. Finally, all signals weredivided by their interquartile ranges to complete the normalizationprocess.

The three-dimensional space construction is subsequently used to studygeometrical and dynamical properties of the system. The pre-processedsignal is subjected to a feature extraction process. The signal ismodeled with the modified matching pursuit (MMP) algorithm to create asparse mathematical model. Characteristics of the model, includingresidue quantification, were included in the feature set. The vVCG wasfurther quantified by creating an encapsulating alpha shape, or aspecific Delaunay triangulation. This triangulation has associatedcharacteristics that composed the feature vector representing therecording interval.

Delaunay triangulations are triangulations on a set of points such thatno point is within the circumcircle of any triangle in thetriangulation, and the minimum angle of all the angles in each trianglein the triangulation is maximized.

An alpha shape adds a further constraint; this triangulation requiresthe specification of an alpha radius, and only pairs of points whosedistance is less than the alpha radius may be connected by an edge. Thealpha radius in this feature extraction process was determinedobservationally to be 0.6, which allowed for sufficient encapsulation ofthe vVCG while still creating appropriate sparsity in the triangulationin areas of reduced point density.

This feature extraction was performed using SHARCNET (a consortium ofcolleges, universities and research institutes operating a network ofhigh-performance computer clusters across southwestern, central andnorthern Ontario, Canada).

At the conclusion of the feature creation process, there were 250features to represent the 10-second recording interval.

After the feature extraction, the ANN and random forest algorithms wereinvoked to create the predictors. An ANN was trained using the labeledfeature vectors from 15% of the recording intervals, where therelatively small training percentage was chosen to minimize thepotential to overfit and allow for generalizability. Selecting arelatively small segment of the available data for training, andperforming well on the larger test set, requires that the ANN leverageoverarching signatures in the data rather than patterns unique to thetraining set. The ANN had an input dimensionality of 250 neurons, asingle hidden layer that expanded that dimensionality by a factor ofthree to a total of 750 neurons, and then a single neuron in the outputlayer to represent the prediction of DMO-exposed or chemical naïve. Eachneuron in the ANN contained the hyperbolic tangent activation function.The ANN sought to minimize the root mean squared error between theprediction and the recording interval class on the training data usingstochastic gradient descent. Low learning rate and momentum parameterswere used to allow the ANN to evolve gradually, and input corruption(the addition of noise on the incoming features) as well as dropout(noise internal to the ANN) were used to control for overfitting, whichoccurs when the ANN is highly specific to the training data and isunlikely to generalize to novel data. The ANN was a customimplementation in the MATLAB language. The performance of this ANN whenit was applied to the withheld 85% of the data (351,520 data points intotal) is shown below in Table 2, and the corresponding ROC curve isplotted in FIG. 18.

With respect to the random forest, the MATLAB function TreeBagger wasused in which the specification of the number of features to sample ateach node in the tree was selected from all the features, therebyexploiting the varying levels of information present within thefeatures, and yet gain the generalization benefit from the randomsampling of the training data for each member of the random forest.

Heart rate variability was also calculated on the pre-processed signals.The detection of the R peaks was performed through the identification ofhigh-confidence R peaks, or those that exist in the top decile of thesignal, and the creation of templates based on these high-confidencepeaks. A template matching procedure was then executed on the signal,identifying the peaks of segments of the signal with a high degree ofsimilarity to the templates, resulting in an expanded set of R peaks.The confidence of these R peaks was then quantified by calculating theabsolute difference between the maximum R-R interval and the minimum R-Rinterval, and dividing by the maximum R-R interval in order to create anormalized peak confidence score. The threshold on the score was set to0.7 (not to be confused with the generalized Hurst exponent threshold,also set to 0.7), in order to allow for the heart rate variability thatis to be expected in any mammalian cardiovascular system, yet remove anypeak detections that are likely to have missed a peak or identified anadditional peak, and thus depressing the confidence score. HRV can thenbe calculated on R-R intervals derived from the identified peaks, usingthe methods already described.

Although the invention is described in terms of particular embodimentsand applications, one of ordinary skill in the art, in light of thisteaching, can generate additional embodiments and modifications withoutdeparting from the spirit of or exceeding the scope of the exampledinvention. Accordingly, it is understood that the drawings anddescriptions herein are proffered by way of example to facilitatecomprehension of the invention and should not be construed to limit thescope thereof.

For example, in view of the exemplified method and system, a singleelectrode lead measurements may be used in analysis that conventionallyuse data from multiple lead measurements, e.g., to assess certainphysiologic characteristics or disease.

In addition, if the current observations in rat are clinicallytranslatable, there are important implications with respect to thelong-term cardiac health after spontaneous post-partum resolution ofCHD. For example, the ability to identify latent markers of futurecardiac dysfunction using only ECG signals would be a significantcost-effective step forward for the identification of at-riskindividuals who typically do not outwardly display an inherent cardiacrisk.

In addition, the animal model upon which these exemplified experimentsare based, demonstrate that there are important implications withrespect to the long-term cardiac health after spontaneous post-partumresolution of CHD.

What is claimed is:
 1. A system comprising: a biopotential measuringequipment configured to non-invasively capture, from one or moreelectrical leads placed on a subject, biopotential signals as abiopotential signal data set, wherein the captured biopotential signalsare used to detect one or more diagnosable pathologies of the subject,wherein the detection operation comprises: generating, via a processor,one or more fractional derivative data sets by numerically performing,for each of a first fractional derivative data set and a secondfractional derivative signal data set of the one or more fractionalderivative data sets, one or more fractional derivative operations onthe biopotential signal data set in a frequency domain and converting aresult of the one or more fractional derivative operations to a timedomain signal data set, wherein each of the first generated fractionalderivative signal data set and the second generated fractionalderivative signal data set comprises a same length and a same samplingfrequency as the biopotential signal data set; and generating, via theprocessor, a three-dimensional space data set wherein each correspondingvalue of the biopotential signal data set, the first fractionalderivative signal data set, and the second fractional derivative signaldata set forms a three-dimensional point in said space data set, whereingeometric features and/or dynamical properties of the three-dimensionalspace data set are used as variables representative of the subject in amachine learning operation to detect one or more diagnosable pathologiesof the subject.
 2. The system of claim 1, wherein the first fractionalderivative signal data set is generated by performing a first numericfractional derivative operation of a first pre-defined order value onthe biopotential signal data set in the frequency domain and byperforming an inversed transformation operation on the first fractionalderived signal data set to convert the first fractional derived signaldata set to a time domain signal data set.
 3. The system of claim 2,wherein the inversed transformation operation comprises an inversed FastFourier Transform operation.
 4. The system of claim 1, wherein thesecond fractional derivative signal data set is generated by performinga second numeric fractional derivative operation of a second pre-definedorder value on the biopotential signal data set and by performing aninversed transformation operation on the second fractional derivedsignal data to convert the second fractional derived signal data set toa time domain signal data set.
 5. The system of claim 1, wherein each ofthe first fractional derivative signal data set and the secondfractional derivative signal data set comprises a time domain signaldata.
 6. The system of claim 1, wherein the first fractional derivativesignal data set is generated by performing a fractional derivativeoperation of an order of pi/2 or an order of 0.5.
 7. The system of claim1, wherein the geometric features and/or the dynamical properties of thethree-dimensional space data set are generated by performing a modifiedmatching pursuit (MMP) algorithm operation on the three-dimensionalpoint in said space data set.
 8. The system of claim 1, wherein thebiopotential signal data set is associated with a biopotential signalcollected from a single electrical lead.
 9. The system of claim 1,wherein the electrical-lead-collected measurements of the at least onebiopotential signal are collected at a location selected from the groupconsisting of a chest line of the subject, a waistline of the subject, awrist of the subject, a pelvic line of the subject, a neck of thesubject, an ankle of the subject, a forehead of the subject, and an armline of the subject.
 10. The system of claim 1, wherein the detectionoperation comprises: generating, via the processor, an alpha shape or aDelaunay triangle mesh of the three-dimensional point in said space dataset, wherein the geometric features and/or the dynamical properties ofthe three-dimensional space data set include the geometric features ofthe alpha shape or the Delaunay triangle mesh.
 11. The system of claim1, wherein the biopotential signal data set compriseselectrocardiographic data.
 12. The system of claim 1, wherein themachine learning operation comprises an artificial neural networkalgorithm or a regression random forest algorithm.
 13. The system ofclaim 1, wherein the biopotential signal data set are received, over anetwork, at an analysis system that performs the detection operation.14. The system of claim 13, wherein the analysis system receives thebiopotential signal data set from a storage area network (SAN) storedthere at.
 15. A method comprising: capturing, via a biopotentialmeasuring equipment, one or more biopotential signals from one or moreelectrical leads placed on a subject; and causing, by a processor, theacquired one or more biopotential signal to be stored, wherein thestored biopotential signal are analyzed to detect one or morediagnosable pathologies of the subject, wherein the detection operationcomprises: generating, via a processor, a first fractional derivativedata set and a second fractional derivative signal data set bynumerically performing, for each of the first fractional derivative dataset and the second fractional derivative signal data set, one or morefractional derivative operations on the biopotential signal data set ina frequency domain and converting a result of the one or more fractionalderivative operations to a time domain signal data set, wherein each ofthe first generated fractional derivative signal data set and the secondgenerated fractional derivative signal data set comprises a same lengthand a same sampling frequency as the biopotential signal data set; andgenerating, via the processor, a three-dimensional space data setwherein each corresponding value of the biopotential signal data set,the first fractional derivative signal data set, and the secondfractional derivative signal data set forms a three-dimensional point insaid space data set, wherein geometric features and/or dynamicalproperties of the three-dimensional space data set are used as variablesrepresentative of the subject in a machine learning operation to detectone or more diagnosable pathologies of the subject.
 16. The method ofclaim 15 further comprising: transmitting, over a network, the storedbiopotential signal to a storage area network (SAN), wherein an analysissystem receives the stored biopotential signal data set from the storagearea network (SAN) to detect the one or more diagnosable pathologies ofthe subject.
 17. The method of claim 15, wherein the one or morediagnosable pathologies include a congenital heart defect (CHD) in amammal.
 18. The method of claim 15, wherein the first fractionalderivative signal data set is generated by performing a first numericfractional derivative operation of a first pre-defined order value onthe biopotential signal data set in the frequency domain and byperforming an inversed transformation operation on the first fractionalderived signal data set to convert the first fractional derived signaldata set to a time domain signal data set, and wherein the secondfractional derivative signal data set is generated by performing asecond numeric fractional derivative operation of a second pre-definedorder value on the biopotential signal data set and by performing aninversed transformation operation on the second fractional derivedsignal data to convert the second fractional derived signal data set toa time domain signal data set.
 19. The method of claim 15, wherein theelectrical-lead-collected measurements of the at least one biopotentialsignal are collected at a location selected from the group consisting ofa chest line of the subject, a waistline of the subject, a wrist of thesubject, a pelvic line of the subject, a neck of the subject, an ankleof the subject, a forehead of the subject, and an arm line of thesubject.
 20. The method of claim 15, wherein the geometric featuresand/or the dynamical properties of the three-dimensional space data setare generated by performing a modified matching pursuit (MMP) algorithmoperation on the three-dimensional point in said space data set, andwherein the machine learning operation comprises an artificial neuralnetwork algorithm or a regression random forest algorithm, and whereinthe detection operation comprises generating, via the processor, analpha shape or a Delaunay triangle mesh of the three-dimensional pointin said space data set, wherein the geometric features and/or thedynamical properties of the three-dimensional space data set include thegeometric features of the alpha shape or the Delaunay triangle mesh.